基于神经网络的地面目标威胁度分析

空中火力打击对目标威胁度的判定是制定计划的关键步骤。为了提高评估目标威胁度的准确性,在建立威胁度评估指标体系的基础上,利用神经网络模型对生成的训练样本进行训练和测试,建立威胁度评估模型。结果表明:该模型能提高威胁估计算法的准确性和适应性,克服评估中的人为因素影响,对作战计划的制定有一定的借鉴意义。

Analysis of Ground Targets Threatening Level Based on Neural Network

Target threatening level decision is a crucial method in planning air strike.For improving its accuracy,based on index system of threatening level,BP Neural Network has been used,and after trained and tested by training samples,the BP NN model is been built.The result shows that this model overcomes human factors,and improves the accuracy and adaptability of threatening evaluation,which can help staff officers make operation plans.